Just a test for creating a modal

This dataset compiles a wide range of information aimed at understanding, analyzing, and predicting depression levels among students. It is designed for research in psychology, data science, and education, providing insights into factors that contribute to student mental health challenges and aiding in the design of early intervention strategies.

The Finance & Economics Dataset provides daily financial and macroeconomic data, including stock market prices, GDP growth, inflation, interest rates, consumer spending, exchange rates, and more. It is designed for use in: Financial Market Analysis – Track stock index movements and trading volumes. Macroeconomic Research – Study economic trends, including inflation and GDP growth. Investment Decision Making – Evaluate interest rates, corporate profits, and consumer confidence. Machine Learning & Predictive Analytics – Develop forecasting models for economic indicators. This dataset is valuable for economists, investors, data scientists, researchers, and policymakers.

Discover the YouTube Comments Dataset, a fully cleaned and preprocessed collection of YouTube video comments. This dataset is perfect for sentiment analysis, natural language processing, and text-based machine learning projects. With all irrelevant data already removed and cleaning steps thoroughly performed, it provides clean, structured information, allowing you to focus solely on insights and analysis. Dive into the world of social media trends and user behavior with this ready-to-use dataset!

9975 rows data set of movies for description or synopsis.

Movies Dataset TMDB (Top Rated) Dataset Description: This dataset contains top-rated movies retrieved from The Movie Database (TMDB) API. It includes essential details such as: Movie title TMDB rating Release date Overview Genre(s) Original language Popularity score Vote count This dataset can be used for exploratory data analysis (EDA), visualization, and machine-learning projects related to movie trends, ratings, and audience preferences. Source & Attribution: The data is sourced from The Movie Database (TMDB). TMDB does not endorse or certify this dataset. The dataset is provided under the Attribution 4.0 International (CC BY 4.0) license, which allows sharing and adaptation with proper credit. Usage: You can use this dataset for research, analysis, and educational purposes. If you use this dataset in any publication or project.

用来测试中文

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this database is create in 20250214 for test

KEY TAKEAWAYS • The team is preparing for testing the new iOS build version 1.33.0. • There was a discussion on how to handle testing new features and documenting test cases. • The possibility of back-end issues affecting app displays was emphasized. • Team members are encouraged to proactively communicate blockages in testing. • Clear communication and validation processes for test cases are established. SUMMARY • The meeting opened with New Year greetings, followed by a discussion about the upcoming iOS build for testing. • There was a shared document that included a single decision point requiring further clarification about app access issues. • Participants acknowledged new testing processes and highlighted the need for effective communication regarding blockers and test cases. • Specific issues such as duplicate store hours, phone number visibility, and back-end discrepancies were raised and discussed. • Team members were encouraged to document new test cases for features and provide validation where necessary to avoid duplicative work. NEXT STEPS • Confirm receipt of the new iOS build and begin testing as outlined. • Each member should document any new features tested and share with the team for validation. • Regularly check for updates on the status of identified back-end issues and communicate findings. • Ensure proactive communication about any blockers encountered during testing. • Follow up with the back-end team regarding the issues raised about the data discrepancies observed in the app.

Test cases

This is a Test Project

---Before you use it--- I've noticed a large amount of 'max' (15) values for the availability column. My theory is, Adidas restocks them at random order/interval. After filtering out that value, I was able to use the set more effectively

Fidelis is testing the CSV File Upload

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